System and method for predicting behaviors of detected objects through environment representation

ABSTRACT

Aspects of the invention relate generally to autonomous vehicles. The features described improve the safety, use, driver experience, and performance of these vehicles by performing a behavior analysis on mobile objects in the vicinity of an autonomous vehicle. Specifically, the autonomous vehicle is capable of detecting nearby objects, such as vehicles and pedestrians, and is able to determine how the detected vehicles and pedestrians perceive their surroundings. The autonomous vehicle may then use this information to safely maneuver around all nearby objects.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/366,828, filed Feb. 6, 2012, the disclosure of which isincorporated herein by reference.

BACKGROUND

Autonomous vehicles use various computing systems to aid in thetransport passengers from one location to another. Some autonomousvehicles may require some initial input or continuous input from anoperator, such as a pilot, driver, or passenger. Other systems, forexample autopilot systems, may be used only when the system has beenengaged, which permits the operator to switch from a manual mode (wherethe operator exercises a high degree of control over the movement of thevehicle) to an autonomous mode (where the vehicle essentially drivesitself) to modes that lie somewhere in between.

BRIEF SUMMARY

In various aspects, the invention provides a vehicle having a steeringdevice (e.g., wheels that turn in the case of an automobile and a rudderin the case of a boat) and engine. The steering device may be controlledby a first user input controller (e.g., a steering wheel in the cockpitof a car), the engine may be controlled by a second user inputcontroller (e.g., accelerator in the case of a car or a throttle in thecase of boat), and both the engine and device may be controlled by aprocessor capable of executing computer instructions. The vehicleincludes one or more sensors (e.g., cameras, radar, laser range finders)for capturing information relating to the environment in which thevehicle is operating. The processor receives data from the sensors and,based in part on data from the sensors or received from external sourcesor both, issues a navigation command, where a navigation commandcomprises a command to the steering device relating to the intendeddirection of the vehicle (e.g., a command to turn the front wheels of acar 10 degrees to the left) or to the engine relating to the intendedvelocity of the vehicle (e.g., a command to accelerate). Navigationcommands may also include commands to brakes to slow the vehicle down,as well as other commands affecting the movement of the vehicle.

In one aspect, sensors are used to detect a plurality of objectsexternal to the vehicle, and data corresponding to the objects is sentto a processor. The processor analyzes the data corresponding to theobjects to identify the object as mobile objects (e.g. automobiles,trucks, pedestrians, bicycles, etc.) or stationary objects (e.g.,signposts, buildings, trees, etc.). The processor may then determine theposition and movement of a first mobile object relative to one or moreof the other detected objects. Based on how the first object perceivesits surroundings, the processor may predict the likely behavior of thefirst object. The vehicle is then capable of orienting itselfautonomously in an intended position and velocity based at least in parton the likely behavior of the objects.

In another aspect, the vehicle may orient itself so as to reduce therisk of potential collisions with the first object as the object travelsin the predicted manner. For example, if a first detected object istraveling along a first lane of traffic, and the predicted likelybehavior of the detected object includes the first object changing fromthe first lane of traffic to a second lane of traffic. The command toorient the vehicle may include having the vehicle travel in a lane oftraffic other than the second lane of traffic.

In yet another aspect, the processor may select a second object from theplurality of objects and determine the position and movement of theplurality of objects relative to the second object. The processor maythen predict the likely behavior of the second object based on thedetermined position and movement of the plurality of objects and basedon the predicted likely behavior of the first object. The processor thenprovides a command to orient the vehicle relative to the second objectbased on the likely behavior of the second object.

In still another aspect, the processor adjusts the predicted likelybehavior of the first object based on the predicted likely behavior ofthe second object and provides a command to orient the vehicle relativeto the first object based on the adjusted likely behavior of the firstobject.

In yet another aspect, the processor receives a request to navigatebetween a first location and a second location. The processor thenautonomously navigates the vehicle between the first location and asecond location, while simultaneously determining the likely behavior ofdetected objects and providing a command to orient the vehicle relativeto those detected objects.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a functional diagram of an autonomous navigation system inaccordance with an aspect of the disclosure.

FIG. 2 is an exemplary design of the interior of an autonomous vehiclein accordance with an aspect of the disclosure.

FIG. 3 is a view of the exterior of an exemplary vehicle in accordancewith an aspect of the disclosure.

FIGS. 4A-4D are views of the sensor fields for an autonomous vehicle.

FIG. 5 is a view of an autonomous vehicle traveling autonomously inproximity to other vehicles.

FIG. 6 is a view of an autonomous vehicle traveling autonomously inproximity to other vehicles and pedestrians.

FIG. 7 is a flow chart in accordance with an aspect of the disclosure.

FIG. 8 is a functional diagram of a communication system in accordancewith an aspect of the disclosure.

DETAILED DESCRIPTION

Aspects of the disclosure relate generally to an autonomous drivingsystem. In particular, a vehicle implementing the autonomous drivingsystem is capable of detecting and reacting to surrounding objects. Someof the detected objects will be mobile, such as pedestrians,automobiles, and bicycles. As set forth below, the autonomous drivingsystem is operable to identify surrounding objects and determine howthose objects perceive their surroundings. In addition, based on theobject's perceived surroundings, the autonomous driving system maypredict the object's likely movements and behavior. In turn, the vehiclemay react to nearby objects in a way that decreases the likelihood of anaccident and increases the efficiency of travel.

As shown in FIG. 1, an autonomous driving system 100 in accordance withone aspect of the invention includes a vehicle 101 with variouscomponents. While certain aspects of the invention are particularlyuseful in connection with specific types of vehicles, the vehicle may beany type of vehicle including, but not limited to, cars, trucks,motorcycles, busses, boats, airplanes, helicopters, lawnmowers,recreational vehicles, amusement park vehicles, trams, golf carts,trains, and trolleys. The vehicle may have one or more computers, suchas computer 110 containing a processor 120, memory 130 and othercomponents typically present in general purpose computers.

The memory 130 stores information accessible by processor 120, includinginstructions 132 and data 134 that may be executed or otherwise used bythe processor 120. The memory 130 may be of any type capable of storinginformation accessible by the processor, including a computer-readablemedium, or other medium that stores data that may be read with the aidof an electronic device, such as a hard-drive, memory card, ROM, RAM,DVD or other optical disks, as well as other write-capable and read-onlymemories. Systems and methods may include different combinations of theforegoing, whereby different portions of the instructions and data arestored on different types of media.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computer codeon the computer-readable medium. In that regard, the terms“instructions” and “programs” may be used interchangeably herein. Theinstructions may be stored in object code format for direct processingby the processor, or in any other computer language including scripts orcollections of independent source code modules that are interpreted ondemand or compiled in advance. Functions, methods and routines of theinstructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the systemand method is not limited by any particular data structure, the data maybe stored in computer registers, in a relational database as a tablehaving a plurality of different fields and records, XML documents orflat files. The data may also be formatted in any computer-readableformat. By further way of example only, image data may be stored asbitmaps comprised of grids of pixels that are stored in accordance withformats that are compressed or uncompressed, lossless (e.g., BMP) orlossy (e.g., JPEG), and bitmap or vector-based (e.g., SVG), as well ascomputer instructions for drawing graphics. The data may comprise anyinformation sufficient to identify the relevant information, such asnumbers, descriptive text, proprietary codes, references to data storedin other areas of the same memory or different memories (including othernetwork locations) or information that is used by a function tocalculate the relevant data.

The processor 120 may be any conventional processor, such as processorsfrom Intel Corporation or Advanced Micro Devices. Alternatively, theprocessor may be a dedicated device such as an ASIC. Although FIG. 1functionally illustrates the processor, memory, and other elements ofcomputer 110 as being within the same block, it will be understood bythose of ordinary skill in the art that the processor and memory mayactually comprise multiple processors and memories that may or may notbe stored within the same physical housing. For example, memory may be ahard drive or other storage media located in a housing different fromthat of computer 110. Accordingly, references to a processor or computerwill be understood to include references to a collection of processorsor computers or memories that may or may not operate in parallel. Ratherthan using a single processor to perform the steps described herein someof the components such as steering components and decelerationcomponents may each have their own processor that only performscalculations related to the component's specific function.

In various of the aspects described herein, the processor may be locatedremote from the vehicle and communicate with the vehicle wirelessly. Inother aspects, some of the processes described herein are executed on aprocessor disposed within the vehicle and others by a remote processor,including taking the steps necessary to execute a single maneuver.

Computer 110 may include all of the components normally used inconnection with a computer, such as a central processing unit (CPU),memory (e.g., RAM and internal hard drives) storing data 134 andinstructions such as a web browser, an electronic display 142 (e.g., amonitor having a screen, a small LCD touch-screen or any otherelectrical device that is operable to display information), user input(e.g., a mouse, keyboard, touch screen and/or microphone), as well asvarious sensors (e.g. a video camera) for gathering the explicit (e.g. agesture) or implicit (e.g. “the person is asleep”) information about thestates and desires of a person.

The vehicle may also include a geographic position component 144 incommunication with computer 110 for determining the geographic locationof the device. For example, the position component may include a GPSreceiver to determine the device's latitude, longitude and/or altitudeposition. Other location systems such as laser-based localizationsystems, inertial-aided GPS, or camera-based localization may also beused to identify the location of the vehicle. The location of thevehicle may include an absolute geographical location, such as latitude,longitude, and altitude as well as relative location information, suchas location relative to other cars immediately around it which can oftenbe determined with less noise than absolute geographical location.

The device may also include other features in communication withcomputer 110, such as an accelerometer, gyroscope or anotherdirection/speed detection device 146 to determine the direction andspeed of the vehicle or changes thereto. By way of example only,acceleration device 146 may determine its pitch, yaw or roll (or changesthereto) relative to the direction of gravity or a plane perpendicularthereto. The device may also track increases or decreases in speed andthe direction of such changes. The device's provision of location andorientation data as set forth herein may be provided automatically tothe user, computer 110, other computers and combinations of theforegoing.

The computer 110 may control the direction and speed of the vehicle bycontrolling various components. By way of example, if the vehicle isoperating in a completely autonomous mode, computer 110 may cause thevehicle to accelerate (e.g., by increasing fuel or other energy providedto the engine), decelerate (e.g., by decreasing the fuel supplied to theengine or by applying brakes) and change direction (e.g., by turning thefront two wheels).

Computer 110 may also control status indicators 138, in order to conveythe status of the vehicle and its components to a passenger of vehicle101. For example, vehicle 101 may be equipped with a display 225 fordisplaying information relating to the overall status of the vehicle,particular sensors, or computer 110 in particular. The display 225 mayinclude computer generated images of the vehicle's surroundingsincluding, for example, the status of the computer (cruise), the vehicleitself 410, roadways 420, intersections 430, as well as other objectsand information.

Computer 110 may use visual or audible cues to indicate whether computer110 is obtaining valid data from the various sensors, whether thecomputer is partially or completely controlling the direction or speedof the car or both, whether there are any errors, etc. Vehicle 101 mayalso include a status indicating apparatus, such as status bar 230, toindicate the current status of vehicle 101. In the example of FIG. 2,status bar 230 displays “D” and “2 mph” indicating that the vehicle ispresently in drive mode and is moving at 2 miles per hour. In thatregard, the vehicle may display text on an electronic display,illuminate portions of vehicle 101, or provide various other types ofindications. In addition, the computer may also have external indicatorswhich indicate whether, at the moment, a human or an automated system isin control of the vehicle, that are readable by humans, other computers,or both.

In one example, computer 110 may be an autonomous driving computingsystem capable of communicating with various components of the vehicle.For example, computer 110 may be in communication with the vehicle'sconventional central processor 160 and may send and receive informationfrom the various systems of vehicle 101, for example the braking 180,acceleration 182, signaling 184, and navigation 186 systems in order tocontrol the movement, speed, etc. of vehicle 101. In addition, whenengaged, computer 110 may control some or all of these functions ofvehicle 101 and thus be fully or partially autonomous. It will beunderstood that although various systems and computer 110 are shownwithin vehicle 101, these elements may be external to vehicle 101 orphysically separated by large distances.

FIG. 2 depicts an exemplary design of the interior of an autonomousvehicle. The autonomous vehicle may include all of the features of anon-autonomous vehicle, for example: a steering apparatus, such assteering wheel 210; a navigation display apparatus, such as navigationdisplay 215; and a gear selector apparatus, such as gear shifter 220.

The vehicle may include components for detecting objects external to thevehicle such as other vehicles, obstacles in the roadway, trafficsignals, signs, trees, etc. The detection system may include lasers,sonar, radar, cameras or any other detection devices. For example, ifthe vehicle is a small passenger car, the car may include a lasermounted on the roof or other convenient location. In one aspect, thelaser may measure the distance between the vehicle and the objectsurfaces facing the vehicle by spinning on its axis and changing itspitch. The vehicle may also include various radar detection units, suchas those used for adaptive cruise control systems. The radar detectionunits may be located on the front and back of the car as well as oneither side of the front bumper. In another example, a variety ofcameras may be mounted on the car at distances from one another whichare known so that the parallax from the different images may be used tocompute the distance to various objects which are captured by 2 or morecameras. These sensors allow the vehicle to understand and potentiallyrespond to its environment in order to maximize safety for passengers aswell as objects or people in the environment.

Many of these sensors provide data that is processed by the computer inreal-time, that is, the sensors may continuously update their output toreflect the environment being sensed at or over a range of time, andcontinuously or as-demanded provide that updated output to the computerso that the computer can determine whether the vehicle's then-currentdirection or speed should be modified in response to the sensedenvironment.

FIG. 3 illustrates a particular embodiment for a small passenger vehicle301 that includes lasers 310 and 311, mounted on the front and top ofthe vehicle, respectively. Laser 310 may have a range of approximately150 meters, a thirty degree vertical field of view, and approximately athirty degree horizontal field of view. Laser 311 may have a range ofapproximately 50-80 meters, a thirty degree vertical field of view, anda 360 degree horizontal field of view. The lasers may provide thevehicle with range and intensity information which the computer may useto identify the location and distance of various objects. In one aspect,the lasers may measure the distance between the vehicle and the objectsurfaces facing the vehicle by spinning on its axis and changing itspitch.

The vehicle may also include various radar detection units, such asthose used for adaptive cruise control systems. The radar detectionunits may be located on the front and back of the car as well as oneither side of the front bumper. As shown in the example of FIG. 3,vehicle 301 includes radar detection units 320-323 located on the side(only one side being shown), front and rear of the vehicle. Each ofthese radar detection units may have a range of approximately 200 metersfor an approximately 18 degree field of view as well as a range ofapproximately 60 meters for an approximately 56 degree field of view.

In another example, a variety of cameras may be mounted on the vehicle.The cameras may be mounted at predetermined distances so that theparallax from the images of 2 or more cameras may be used to compute thedistance to various objects. As shown in FIG. 3, vehicle 301 may include2 cameras 330-331 mounted under a windshield 340 near the rear viewmirror (not shown). Camera 330 may include a range of approximately 200meters and an approximately 30 degree horizontal field of view, whilecamera 331 may include a range of approximately 100 meters and anapproximately 60 degree horizontal field of view.

Each sensor may be associated with a particular sensor field in whichthe sensor may be used to detect objects. FIG. 4A is a top-down view ofthe approximate sensor fields of the various sensors. FIG. 4B depictsthe approximate sensor fields 410 and 411 for lasers 310 and 311,respectively based on the fields of view for these sensors. For example,sensor field 410 includes an approximately 30 degree horizontal field ofview for approximately 150 meters, and sensor field 411 includes a 360degree horizontal field of view for approximately 80 meters.

FIG. 4D depicts the approximate sensor fields 420A-423B and for radardetection units 320-323, respectively, based on the fields of view forthese sensors. For example, radar detection unit 320 includes sensorfields 420A and 420B. Sensor field 420A includes an approximately 18degree horizontal field of view for approximately 200 meters, and sensorfield 420B includes an approximately 56 degree horizontal field of viewfor approximately 80 meters. Similarly, radar detection units 321-323include sensor fields 421A-423A and 421B-423B. Sensor fields 421A-423Ainclude an approximately 18 degree horizontal field of view forapproximately 200 meters, and sensor fields 421B-423B include anapproximately 56 degree horizontal field of view for approximately 80meters. Sensor fields 421A and 422A extend passed the edge of FIGS. 4Aand 4D.

FIG. 4C depicts the approximate sensor fields 430-431 cameras 330-331,respectively, based on the fields of view for these sensors. Forexample, sensor field 430 of camera 330 includes a field of view ofapproximately 30 degrees for approximately 200 meters, and sensor field431 of camera 430 includes a field of view of approximately 60 degreesfor approximately 100 meters.

In another example, an autonomous vehicle may include sonar devices,stereo cameras, a localization camera, a laser, and a radar detectionunit each with different fields of view. The sonar may have a horizontalfield of view of approximately 60 degrees for a maximum distance ofapproximately 6 meters. The stereo cameras may have an overlappingregion with a horizontal field of view of approximately 50 degrees, avertical field of view of approximately 10 degrees, and a maximumdistance of approximately 30 meters. The localization camera may have ahorizontal field of view of approximately 75 degrees, a vertical fieldof view of approximately 90 degrees and a maximum distance ofapproximately 10 meters. The laser may have a horizontal field of viewof approximately 360 degrees, a vertical field of view of approximately30 degrees, and a maximum distance of 100 meters. The radar may have ahorizontal field of view of 60 degrees for the near beam, 30 degrees forthe far beam, and a maximum distance of 200 meters.

The sensors described may be used to identify, track and predict themovements of pedestrians, bicycles, other vehicles, or objects in theroadway. For example, the sensors may provide the location and shapeinformation of objects surrounding the vehicle to computer 110, which inturn may identify the object as another vehicle. The object's currentmovement may be also be determined by the sensor (e.g., the component isa self-contained speed radar detector) or by the computer 110 based oninformation provided by the sensors (e.g., by comparing changes in theobject's position data over time).

The computer may change the vehicle's current path and speed based onthe presence of detected objects. For example, the vehicle mayautomatically slow down if its current speed is 50 mph and it detects,by using its cameras and using optical-character recognition, that itwill shortly pass a sign indicating that the speed limit is 35 mph. Yetfurther, if the computer determines that an object is obstructing theintended path of the vehicle, it may maneuver the vehicle around theobstruction.

Yet further, the vehicle's computer system may predict a detectedobject's expected movement. For example, autonomous driving system 100is operable to predict a another vehicle's future movement based solelyon the other vehicle's instant direction, acceleration/deceleration andvelocity, e.g., that the other vehicle's current direction and movementwill continue. However, system 100 may also predict a detected vehicle'sfuture movement by analyzing data relating to the other vehicle'scurrent surroundings and determining how the other vehicle will likelyrespond to those surroundings. In other words, Autonomous driving system100 uses an object-centric, view of it's environment, in that the systemdetermines what the other vehicles are perceiving in order to betterpredict how those vehicles will behave.

Once an object is detected, system 100 may determine the type of theobject, for example, a traffic cone, person, car, truck or bicycle, anduse this information to predict the object's future behavior. Objectsmay be identified by using an object classifier 148 which may considervarious characteristics of the detected objects, such as the size of anobject, the speed of the object (bicycles do not tend to go faster than40 miles per hour or slower than 0.1 miles per hour), the heat comingfrom the bicycle (bicycles tend to have rider that emit heat from theirbodies), etc. In addition, the object may be classified based onspecific attributes of the object, such as information contained on alicense plate, bumper sticker, or logos that appear on the vehicle. Inanother example, the scene external to the vehicle need not be segmentedfrom input of the various sensors and nor do objects need to beclassified for the vehicle to take a responsive action. Rather thevehicle may take one or more actions based on the color and/or shape ofan object.

The system may also rely on information that is independent of otherdetected object's in order to predict the an object's next action. Byway of example, if the vehicle determines that another object is abicycle that is beginning to ascend a steep hill in front of thevehicle, the computer may predict that the bicycle will soon slowdown—and will slow the vehicle down accordingly—regardless of whetherthe bicycle is currently traveling at a somewhat high speed.

It will be understood that the forgoing methods of identifying,classifying, and reacting to objects external to the vehicle may be usedalone or in any combination in order to increase the likelihood ofavoiding a collision.

By way of further example, the system may determine that an object nearthe vehicle is another car in a turn-only lane (e.g., by analyzing imagedata that captures the other car, the lane the other car is in, and apainted left-turn arrow in the lane). In that regard, the system maypredict that the other car may turn at the next intersection.

The computer may cause the vehicle to take particular actions inresponse to the predicted actions of the surrounding objects. Forexample, if the computer 110 determines that the other car is turning atthe next intersection as noted above, the computer may slow the vehicledown as it approaches the intersection. In this regard, the predictedbehavior of other objects is based not only on the type of object andits current trajectory, but also based on some likelihood that theobject may obey traffic rules or pre-determined behaviors. In anotherexample, the system may include a library of rules about what objectswill do in various situations. For example, a car in a left-most lanethat has a left-turn arrow mounted on the light will very likely turnleft when the arrow turns green. The library may be built manually, orby the vehicle's observation of other vehicles (autonomous or not) onthe roadway. The library may begin as a human built set of rules whichmay be improved by the vehicle's observations. Similarly, the librarymay begin as rules learned from vehicle observation and have humansexamine the rules and improve them manually. This observation andlearning may be accomplished by, for example, tools and techniques ofmachine learning. The rules library may be included in computer 110 ormay alternatively be accessible to the vehicle 101 via a remote server,such as server 710 of FIG. 7.

In addition to processing data provided by the various sensors, thecomputer may rely on environmental data that was obtained at a previouspoint in time and is expected to persist regardless of the vehicle'spresence in the environment. For example, data 134 may include detailedmap information 136, e.g., highly detailed maps identifying the shapeand elevation of roadways, lane lines, intersections, crosswalks, speedlimits, traffic signals, buildings, signs, real time trafficinformation, or other such objects and information. For example, the mapinformation may include explicit speed limit information associated withvarious roadway segments. The speed limit data may be entered manuallyor scanned from previously taken images of a speed limit sign using, forexample, optical-character recognition. The map information may includethree-dimensional terrain maps incorporating one or more of objectslisted above. For example, the vehicle may determine that another car isexpected to turn based on real-time data (e.g., using its sensors todetermine the current GPS position of another car) and other data (e.g.,comparing the GPS position with previously-stored lane-specific map datato determine whether the other car is within a turn lane).

The computer 110 may also access data 134 relating to certain types ofobjects that the vehicle 101 may encounter. As described above, thesensors of vehicle 101 may be used to identify, track and predict themovements of pedestrians, bicycles, vehicles, or other objects in oraround the roadway. These objects may have particular behavior patternsthat depend on the nature of the object. For example, a bicycle islikely to react differently than a tractor-trailer in a number of ways.Specifically, a bicycle is more likely to make erratic movements whencompared with a tractor-trailer. Accordingly, in predicting an objectsbehavior, computer 110 may access object data 137 that contains numerousobject classifications, such as pedestrians, bicycles, cars,tractor-trailers, etc. For each classification, the object data 137 mayalso contain behavior information that indicates how an object having aparticular classification is likely to behave in a given situation.Vehicle 101 may then autonomously respond to the object based, in part,on the predicted behavior.

In addition to classifying the object, vehicle 101 may track a currentstate of the object. The object's state may include information used todetermine the object's classification, but any type of environmental orcontextual information may be used to determine the object's currentstate, such as the object's speed, route the object has traveled, natureof the roadway on which the object is traveling, any lane changes madeby the object, or the object's use of headlights or blinkers.

FIG. 5 illustrates an example of autonomous vehicle 101 traveling onroad 500. Using the sensors described above, autonomous vehicle 101 iscapable of detecting surrounding vehicles, including vehicles 510, 520,530, 540, and 550. As indicated by directional arrows A1, B1, C1, and D,vehicle 510 and vehicle 520 are currently traveling north along theleft-hand lane, while autonomous vehicle 101 and vehicle 530 aretraveling north in the right-hand lane. In this example, autonomousvehicle 101 detects that tractor trailer 530 is traveling very slowly,and seeks to pass it. Without using an object-centric behavior analysis,autonomous vehicle 101 may detect that none of the surrounding vehiclesare currently traveling in the center, north-bound lane of road 500.Accordingly, autonomous vehicle 101 would determine that it may safelychange from the right-hand lane of road 500 to the center lane, asindicated by arrow C2.

As stated above, system 100 may predict how the surrounding vehicleswill behave based on how those vehicles, or the vehicles' operators,perceive their surroundings. For example, autonomous vehicle 101 iscapable of predicting the likely actions of vehicle 510 by figurativelyplacing itself in the position of vehicle 510, and then determining howit would react given vehicle 510's current surroundings. By using anobject-centric view from the perspective of vehicle 510, autonomousvehicle 101 is better able to predict the actions of vehicle 510, and inturn, better able to avoid potential accidents.

In the case of vehicle 510, nearby objects include vehicles 510, 520,530, 540, and 550, as well as autonomous vehicle 101. Accordingly,autonomous vehicle 101 uses the sensors described above to collectposition and movement data for each of these nearby objects relative tovehicle 510. For example, autonomous vehicle 101 may detect that vehicle520 has turned on it's left-hand blinker or is slowing down as itapproaches the next intersection. Autonomous vehicle 101 may, in turn,predict that vehicle 520 intends to make a left-hand turn, as indicatedby directional arrow A2. Based on this additional data regarding vehicle520, and by accessing the behavior model database 137 shown in FIG. 1,autonomous vehicle 101 is capable of altering it's prediction of vehicle510's future movement. In particular, autonomous vehicle 101 determineshow it would react if placed in vehicle 510's position behind theturning vehicle 520. Given that none of the other nearby vehicles occupythe center-lane in vehicle 510's direction of travel, the behavior modeldatabase 137 may predict that the operator of vehicle 510 will likelyattempt change into the center lane, as indicated by directional arrowB2, in order to avoid being impeded by vehicle 520. Based on vehicle510's predicted lane change, autonomous vehicle 101 may then determinethat it is safer to continue along path C1, instead of changing lanes inaccordance with path C2, in that a lane change could create a potentialcollision with vehicle 510.

Autonomous vehicle 101 may perform a similar, object-centric behaviorprediction for each of the other nearby vehicles. For example,autonomous vehicle 101 may figuratively place itself in the position ofvehicle 520, and determine that given the presence of vehicles 540 and550, vehicle 520 will need to come to a complete stop before making theleft-hand turn along path A2. In turn, autonomous vehicle 101 willincrease the probability of vehicle 510 changing lanes along path B2, asit will be directly behind a stopped vehicle. In this way, autonomousvehicle 101 is able to determine how each nearby vehicle perceives it'ssurroundings and to adjust the predicted behavior of each vehicle basedon the predicted behavior of other nearby vehicles.

Autonomous driving system 100 may use object-centric behavior models fornumerous types, or classifications, of objects, including automobiles,bicycles, or pedestrians. In many instances, it is not practical topredict an object's behavior by determining how autonomous vehicle 101would react if placed in the same situation. For example, a pedestrianor cyclist will not react to it's surroundings in the same way asautonomous vehicle 101. In addition, an autonomously navigated vehiclemay not react the same was as a vehicle controlled by a driver.Accordingly, database 137 may include different object-centric behaviormodels for different classifications of objects, including autonomous ornon-autonomous vehicles.

In FIG. 6, autonomous vehicle 101 is traveling along path A1. Using thesensors described above, autonomous vehicle 101 detects a group ofpedestrians 610, vehicle 620, vehicle 630, and pedestrian signal 640.Based on vehicle 620 activating it's right-hand blinker, autonomousvehicle 101 determines that vehicle 620 intends to turn right, asindicated by arrow C. Based on pedestrian signal 640, as well as therelative position of pedestrians 610, autonomous vehicle predicts thatpedestrians 610 are waiting to cross the street along path B. Inaccordance with one embodiment, autonomous vehicle 101 may accessdatabase 137 shown in FIG. 1, in order to determine whether pedestrians610 will cross the street before or after vehicle 620 has completed it'sright-hand turn. Specifically, autonomous vehicle 101 uses it's sensorsto gather data for all objects in the vicinity of pedestrians 610,including position and movement data for vehicles 620 and 630, as wellas the current signal being provided by pedestrian signal 640.Autonomous vehicle then accesses database 137 to determine whetherpedestrians, in that situation, are likely to cross the street beforevehicle 620 completes it's right-hand turn. Autonomous vehicle 101therefore implements an object-centric view by determining howpedestrians 610 perceive their surroundings, and determining how theywill react to those surroundings. Based on the object-centricpredictions autonomous vehicle 101 will choose between path A1 and A2.Specifically, autonomous vehicle 101 will continue on path A1 if it isdetermined that pedestrians 610 will cross the street after vehicle 620has completed it's right-hand turn. Alternatively, vehicle 101 will takepath A2, if it is determined that pedestrians 610 will cross the streetbefore vehicle 620 travels along path C. In this way, autonomous vehicle101 will not be impeded by vehicle 620 as it waits for pedestrians 610to cross in front of it.

The collection of data for vehicle or pedestrian movements may beaccomplished in any number of ways. For example, the movement ofvehicles may be tracked using satellite imagery, roadside cameras, onboard GPS data, or via sensor data acquired from vehicles similar tovehicle 101. Preferably, the behavior model will be based on a largenumber of tracked objects for each classification of object. In thisway, an accurate behavior model can be created for each classificationof objects.

Autonomous vehicle 101 may transport itself, passengers, and/or cargobetween two locations by following a route. For example, a driver mayinput a destination and activate an autonomous mode of the vehicle. Inresponse, the vehicle's computer 110 may calculate a route using a map,its current location, and the destination. Based on the route (or aspart of the route generation), the vehicle may determine a controlstrategy for controlling the vehicle along the route to the destination.For example, the control strategy may include where to turn, at whatspeeds to travel, what lane to travel in, where to look for trafficsignals, where to stop for intersections or stop signs, etc. Flowdiagram 700 of FIG. 7 provides an example by which vehicle 101 may beautonomously controlled in response to the object-centric predictionmodel. As provided in Block 710, vehicle 101 implements the determinedcontrol strategy by traveling to travel along the route. While travelingin accordance with the control strategy, vehicle 101 detects thepresence of numerous objects within one or more of the vehicle's sensorfields (Block 715).

Upon detecting the objects, the vehicle's computer 110 may classify theobject based on the data received by the vehicle's sensors (Block 720).For example, the sensor data could be used to classify objects as beinga pedestrian, bicycle, sports car, pick-up truck, etc. As describedabove, the vehicle's computer 110 also uses the sensor data to determinethe object's current state, such as speed, acceleration, and laneposition. Upon determining the objects classification and current state,vehicle computer 110 may select one of the detected objects for analysis(Block 725). Computer 110 then determines which of the other detectedobjects can be perceived by the selected object (Block 730). Forexample, if the selected object is a vehicle with a driver, computer 110may determine which of the other objects are within the line of sight ofthe driver. Computer 110 then predicts the likely behavior of theselected object based on the current state of the other perceivedobjects (Block 735). This prediction may be made by determining how theautonomous vehicle would react if placed in the position of the selectedobject. If the selected object is of a type that is substantiallydifferent than the autonomous vehicle, then computer 110 may predict thelikely behavior of the selected object by accessing behavior model data.For example, if the selected object is a bicyclist, computer 110 mayaccess database 137 to determine how a bicyclist would likely react inlight of the surrounding objects. Computer 110 then determines whetherall of the detected objects have been selected under the current controlstrategy (Block 740). If the answer is no, then computer 110 returns toBlock 725, wherein it selects another detected object. Computer 110 thenrepeats Block 730 and 735 for the newly selected object. Once all of thedetected objects have been selected, computer 110 may adjust thepredicted behavior of one or more of the detected objects (Block 745).

An adjustment in predicted behavior may be required due to the likelybehavior of other detected objects. For example, in FIG. 5, autonomousvehicle 101 does not predict a lane change by vehicle 510 until after ithas predicted that vehicle 520 is making a left-hand turn. In addition,the lane change by vehicle 510 will be made more likely, given thatvehicle 520 will need to wait for vehicles 540 and 550 to pass beforemaking it's turn. The predicted behavior of vehicle 510 is thereforeinterrelated to the predicted behavior of other nearby vehicles and mustbe adjusted accordingly. Based on the predicted behavior of the detectedobjects, computer 110 may implement a new or altered control strategy,such as by keeping a greater distance from vehicles that are expected tochange lanes or change speeds in reaction to nearby vehicles (Block750). As the autonomous vehicle travels along the desired route andremains under the control of the autonomous navigation system, computer110 will repeat Block 715 through 750 for all objects detected along theroute (Block 755). By implementing aspects of flow diagram 700, vehicle101 will be able to autonomously react to surrounding vehicles orpedestrians in a way that minimizes the risk of accidents or otherunwanted events.

Vehicle 101 may include one or more user input devices that enable auser to provide information to the autonomous driving computer 110. Forexample, a user, such as passenger 290, may input a destination (e.g.,123 Oak Street) into the navigation system using touch screen 217 orbutton inputs 219. In another example, a user may input a destination byidentifying the destination. In that regard, the computer system mayextract the destination from a user's spoken command (e.g., by statingor inputting “De young museum” as in the example of FIGS. 2 and 3).

The vehicle may also have various user input devices for activating ordeactivating one or more autonomous driving modes. In some examples, thedriver may take control of the vehicle from the computer system byturning the steering wheel, pressing the acceleration or decelerationpedals. The vehicle may further include a large emergency button thatdiscontinues all or nearly all of the computer's decision-making controlrelating to the car's velocity or direction. In another example, thevehicle's shift knob may be used to activate, adjust, or deactivatethese autonomous modes.

Computer 110 may include, or be capable of receiving information from,one or more touch sensitive input apparatuses 140. For example, computer110 may receive input from a user input apparatus and use thisinformation to determine whether a passenger is in contact with, such asby holding or bumping, a particular portion of vehicle 110. The touchsensitive input apparatuses may be any touch sensitive input devicecapable of identifying a force, for example a force resistance tape maybe calibrated to accept or identify a threshold pressure input (such as10 grams of pressure) or a range of pressures (such as 5-20 grams ofpressure).

Again, these inputs may be understood by the computer as commands by theuser to, for example, enter into or exit from one or more autonomousdriving modes. For example, if the vehicle is being operated in anautonomous mode and the driver bumps the steering wheel, if the force isabove the threshold input, the vehicle may go from an autonomous mode toa semi-autonomous mode where the driver has control of at least thesteering.

The various systems described above may be used by the computer tooperate the vehicle and maneuver from one location to another. Forexample, a user may enter destination information into the navigation,either manually or audibly. The vehicle may determine its location to afew inches based on a combination of the GPS receiver data, the sensordata, as well as the detailed map information. In response, thenavigation system may generate a route between the present location ofthe vehicle and the destination.

When the driver is ready to relinquish some level of control to theautonomous driving computer, the user may arm the computer. The computermay be armed, for example, by pressing a button or by manipulating alever such as gear shifter 220. Rather than taking control immediately,the computer may scan the surroundings and determine whether there areany obstacles or objects in the immediate vicinity which may prohibit orreduce the ability of the vehicle to avoid a collision. In this regard,the computer may require that the driver continue controlling thevehicle manually or with some level of control (such as the steering oracceleration) before entering into a fully autonomous mode.

Once the vehicle is able to maneuver safely without the assistance ofthe driver, the vehicle may become fully autonomous and continue to thedestination. It will be understood that the driver may continue toassist the vehicle by controlling, for example, steering or whether thevehicle changes lanes, or the driver may take control of the vehicleimmediately in the event of an emergency.

The vehicle may continuously use the sensor data to identify objects,such as traffic signals, people, other vehicles, and other objects, inorder to maneuver the vehicle to the destination and reduce thelikelihood of a collision. The vehicle may use the map data to determinewhere traffic signals or other objects should appear and take actions,for example, by signaling turns or changing lanes.

Once the vehicle has arrived at the destination, the vehicle may provideaudible or visual cues to the driver. For example, by displaying “Youhave arrived” on one or more of the electronic displays.

In one aspect, the features described above may be used in combinationwith larger vehicles such as trucks, tractor trailers, or passengerbusses. For such vehicles, the system may consider additionalinformation when computing how to control the vehicle safely. Forexample, the physical attributes of a tractor trailer, such as itsarticulation and changing weight, may cause it to maneuver verydifferently than smaller passenger cars. Larger vehicles may requirewider turns or different levels of acceleration and braking in order toavoid collisions and maneuver safely. The computer may consider thegeometry of the vehicle when calculating and executing maneuvers such aslane changes or evasive actions.

The vehicle may be only partially autonomous. For example, the drivermay select to control one or more of the following: steering,acceleration, braking, and emergency braking.

The vehicle may also address driver impairment. For example, if a driverhas been unresponsive, has reduced cognitive abilities, or has beendetected as having fallen asleep, the vehicle may attempt to wake orotherwise prompt the driver to respond. By way of example only, a cameracapturing the driver's face may be used to determine whether thedriver's eyes have remained closed for an extended period of time. Ifthe driver remains unresponsive, the computer may cause the vehicleslow, stop or pull over to a safe location, or may assume control overthe vehicle's direction or speed to avoid a collision.

In another example, the system may be always on with the driverprimarily in control of the vehicle, but only intervene and take actionwhen the vehicle detects that there is an emergency situation. Forexample, if a thick fog reduces the visibility of the driver, thevehicle may use its sensors to detect the presence of objects in thevehicle's path. If the vehicle determines that a collision is imminentyet the driver has taken no action to avoid the collision, the vehiclemay provide the driver with a warning and, absent further correction bythe driver, take one or more evasive actions such as slowing, stoppingor turning the vehicle.

The vehicle may also improve driver performance while the vehicle isunder the control of a driver. For example, if a driver fails tomaintain the vehicle's position within a lane without using a turnsignal, the vehicle may slightly adjust the steering in order to smoothout the vehicle's movements and maintain the vehicle's position withinthe lane. Thus, the vehicle may mitigate the likelihood of undesirableswerving and erratic driving. In another embodiment, if a driver is on acourse to change lanes but has not activated the turn signal, thevehicle may automatically activate the turn signal based on the detectedcourse of the vehicle.

The vehicle may also be used to train and test drivers. For example, thevehicle may be used to instruct new drivers in the real world, but maycontrol various aspects of the ride in order to protect the new driver,people external to the car, and external objects. In another example,new drivers may be required to control the vehicle safely for someperiod. The vehicle may then determine whether the user is a safe driverand thus determine whether the user is ready to be a licensed driver.

The vehicle may also park itself. For example, the map information mayinclude data describing the location of parking spots along a roadway orin a parking lot. The computer may also be configured to use its sensorsto determine potential parking spots, such as causing the vehicle totravel down a road and checking for painted lines along a street thatindicate an open parking space. If computer determines another vehicleor object is not within the spot, the computer may maneuver the vehicleinto the parking spot by controlling the steering and speed of thevehicle. Using the method described above, the vehicle may also classifyany objects that are near the potential parking spot, and position thevehicle within the parking spot based on those surrounding objects. Forexample, the vehicle may position itself closer to an adjacent bicyclethan it would an adjacent truck.

The vehicle may also have one or more user interfaces that allow thedriver to reflect the driver's driving a style. For example, the vehiclemay include a dial which controls the level of risk or aggressivenesswith which a driver would like the computer to use when controlling thevehicle. For example, a more aggressive driver may want to change lanesmore often to pass cars, drive in the left lane on a highway, maneuverthe vehicle closer to the surrounding vehicles, and drive faster thanless aggressive drivers. A less aggressive driver may prefer for thevehicle to take more conservative actions, such as somewhat at or belowthe speed limit, avoiding congested highways, or avoiding populatedareas in order to increase the level of safety. By manipulating thedial, the thresholds used by the computer to calculate whether to passanother car, drive closer to other vehicles, increase speed and the likemay change. In other words, changing the dial may affect a number ofdifferent settings used by the computer during its decision makingprocesses. A driver may also be permitted, via the user interface 225,to change individual settings that relate to the driver's preferences.In one embodiment, insurance rates for the driver or vehicle may bebased on the style of the driving selected by the driver.

Aggressiveness settings may also be modified to reflect the type ofvehicle and its passengers and cargo. For example, if an autonomoustruck is transporting dangerous cargo (e.g., chemicals or flammableliquids), its aggressiveness settings may be less aggressive than a carcarrying a single driver—even if the aggressive dials of both such atruck and car are set to “high.” Moreover, trucks traveling across longdistances over narrow, unpaved, rugged or icy terrain or vehicles may beplaced in a more conservative mode in order reduce the likelihood of acollision or other incident.

In another example, the vehicle may include sport and non-sport modeswhich the user may select or deselect in order to change theaggressiveness of the ride. By way of example, while in “sport mode”,the vehicle may navigate through turns at the maximum speed that issafe, whereas in “non-sport mode”, the vehicle may navigate throughturns at the maximum speed which results in g-forces that are relativelyimperceptible by the passengers in the car.

The vehicle's characteristics may also be adjusted based on whether thedriver or the computer is in control of the vehicle. For example, when aperson is driving manually the suspension may be made fairly stiff sothat the person may “feel” the road and thus drive more responsively orcomfortably, while, when the computer is driving, the suspension may bemade such softer so as to save energy and make for a more comfortableride for passengers.

The vehicle may include a sleeping mode that allows the driver to givefull control of the vehicle to the computer so that the driver may sleepor reduce his or her focus on the roadway. For example, the vehicle mayinclude a user input device that allows the user to input informationsuch as the duration of the sleep mode, e.g., 20 minutes, 4 hours, 8hours, etc. In response, the vehicle may drive slower or on lesstraveled roadways, select a route that will get the driver to thedestination in the identified period, or select a route which that avoidbumps or other disturbances to the driver.

The driver may also select to have his or her vehicle communicate withother devices. As shown in FIG. 8, vehicle 101 may communicate over anetwork 820 with devices such as a remote server 810, a personalcomputer 730, a mobile device 740, or another autonomous vehicle 802. Inaddition, vehicles, such as vehicle 101 and vehicle 102, may wirelesslytransmit information directly to nearby vehicles using radio, cellular,optical or other wireless signals. Alternatively, vehicles maycommunicate with each via nodes that are shared among multiple vehicles,e.g., by using cell towers to call other cars or transmit and sendinformation to other cars via the Internet. The transmitted informationbetween vehicles may include, for example, data describing the vehicleor the vehicle's environment.

In one example, a driver of a first vehicle may select an option toallow other vehicles on the roadway to transmit information from thevehicle's sensors or computer. This information may include detailsabout the first vehicle's environment such as detected objects, trafficconditions, or construction. The information transmitted to othervehicles may be sensor data unprocessed by the first computer orinformation previously processed by the first computer in order toreduce the time needed to obtain and process the information at a secondvehicle. If the second autonomous vehicle is behind the first vehicle,it may use the information to determine how to maneuver the vehicle. Byway of example, if the first vehicle is only a few car lengths in frontof the second vehicle and it detects a moving object, the first vehiclemay transmit information relating to the moving object to the secondvehicle. If the second vehicle determines that the object is movingtowards the second vehicle's path, the second vehicle may slow down. Yetfurther, if the second vehicle is a few miles behind the first vehicleand the first vehicle determines that it is in a traffic jam (e.g., bydetermining that its speed is substantially less than the road's speedlimit), the second vehicle may select an alternate route.

In addition to cooperatively driving together in lines, autonomousvehicles may also communicate in order to increase convenience andsafety on the roadways. For example, autonomous vehicles may be able todouble (two vehicles in a row) and triple park (three vehicles in a row)next to other autonomous vehicles. When a driver would like to use avehicle which is parked in or surrounded by other autonomous vehicles,the driver's vehicle may send a signal instruction the other vehicles tomove out of the way. The vehicles may respond by cooperativelymaneuvering to another location in order to allow the driver's vehicleto exit and may return to park again.

In another example, the cooperation mode may be used to promote smarterroad crossings. For example, if several autonomous vehicles areapproaching and intersection, the right-of-way problem, or which vehicleshould be next to enter the intersection, may be calculated anddetermined cooperatively among the several vehicles. In another example,traffic signals may change quickly, such as within only a few seconds orless, to allow more vehicles to pass through an intersection in multipledirections. The vehicle may only need the traffic signal to be green fora second or less in order to pass through the intersection at highspeeds.

Vehicle 101 may also receive updated map or object data via network 820.For example, server 810 may provide vehicle 101 with new data relatingto object classifications and behavior model information. Computersystem 110, of FIG. 1, may then be updated, and the new data may be usedin controlling the vehicle autonomously, such as through implementationof flow diagram 700.

As these number and usage of these autonomous vehicles increases,various sensors and features may be incorporated into the environment toincrease the perception of the vehicle. For example, low-cost beacontransmitters may be placed on road signs, traffic signals, roads orother highway infrastructure components in order to improve thecomputer's ability to recognize these objects, their meaning, and state.Similarly, these features may also be used to provide additionalinformation to the vehicle and driver such as, whether the driver isapproaching a school or construction zone. In another example, magnets,RFID tags or other such items may be placed in the roadway to delineatethe location of lanes, to identify the ground speed vehicle, or increasethe accuracy of the computer's location determination of the vehicle.

Autonomous vehicles may also be controlled remotely. For example, if thedriver is asleep, the sensor data may be sent to a third party so thatvehicle may continue to have a responsive operator. While delay andlatency may make this type of telemetry driving difficult, it may forexample be used in emergency situations or where the vehicle has gottenitself stuck. The vehicle may send data and images to a central officeand allow a third party to remotely drive the vehicle for a short perioduntil the emergency has passed or the vehicle is no longer stuck.

As these and other variations and combinations of the features discussedabove can be utilized without departing from the invention as defined bythe claims, the foregoing description of exemplary embodiments should betaken by way of illustration rather than by way of limitation of theinvention as defined by the claims. It will also be understood that theprovision of examples of the invention (as well as clauses phrased as“such as,” “e.g.”, “including” and the like) should not be interpretedas limiting the invention to the specific examples; rather, the examplesare intended to illustrate only some of many possible aspects.

The invention claimed is:
 1. A method for autonomous control of a firstvehicle having one or more processors configured to control the firstvehicle in an autonomous driving mode, the method comprising: receiving,from an object sensing system of the vehicle including one or moresensors, information identifying a plurality of mobile objects externalto the first vehicle as well as position and movement of each of theplurality of mobile objects; identifying, by one or more processors, anobject from the plurality of mobile objects corresponding to a secondvehicle; determining, by the one or more processors, how the firstvehicle would behave if the first vehicle were placed in the position ofthe object; using, by the one or more processors, the determination topredict a likely future behavior of the object; and controlling, by theone or more processors, the first vehicle in the autonomous driving modebased on the predicted likely future behavior of the first object. 2.The method of claim 1, wherein the object is a second vehicle, theposition of the second vehicle indicates that the second vehicle is in aturning lane, and wherein the predicted likely future behavior of thesecond vehicle is turning at an intersection.
 3. The method of claim 2,wherein controlling the first vehicle includes slowing the first vehicledown as the first vehicle approaches the intersection.
 4. The method ofclaim 1, further comprising: predicting a likely future behavior of asecond object of the plurality of moving objects, wherein the secondobject is a an object other than a vehicle, using an object-type basedprediction model, the movement of the second object, and the position ofthe second object; and wherein controlling the vehicle is further basedon the predicted likely future behavior of the second object.
 5. Themethod of claim 4, further comprising, prior to predicting the likelyfuture behavior of the second object, determining that a type of thesecond object is a type other than a vehicle.
 6. The method of claim 5,further comprising, after determining that the second object is the typeother than a vehicle, selecting the object-type based prediction modelfrom a plurality of object-type based prediction model each associatedwith an object type, such that the associated object type of theselected object-type based prediction model corresponds to the typeother than a vehicle.
 7. The method of claim 4, wherein the secondobject is a bicycle, and the position of the bicycle indicates that thebicycle is beginning to ascend a hill in front of the vehicle, andwherein the predicted likely future behavior of the bicycle is slowingdown.
 8. The method of claim 7, wherein controlling the first vehicleincludes slowing the first vehicle down regardless of the accelerationof the bicycle.
 9. The method of claim 4, wherein the second object is apedestrian, and the position of the pedestrian indicates that thepedestrian is approaching a crosswalk, and wherein the predicted likelyfuture behavior of the pedestrian is crossing a road using thecrosswalk.
 10. The method of claim 9, wherein predicting a likely futurebehavior of the second object is further based on the position andmovement of at least one other object of the plurality of movingobjects.
 11. The method of claim 10, wherein the at least one otherobject of the plurality of moving objects is a vehicle passing throughthe crosswalk, and wherein the predicted likely future behavior of thepedestrian is crossing the road using the crosswalk after the vehiclehas passed through the crosswalk.
 12. The method of claim 1, whereindetermining how the first vehicle would behave is further based on theposition and movement of other objects of the plurality of movingobjects.
 13. The method of claim 12, wherein the object is a secondvehicle, one of the other objects is a third vehicle in front of thesecond vehicle and making a turn, and the predicted likely futurebehavior of the third vehicle is changing to an adjacent lane.
 14. Themethod of claim 13, wherein controlling the first vehicle includes notchanging to the adjacent lane.
 15. The method of claim 12, wherein theobject is a second vehicle and the other objects of the plurality ofmoving objects includes at least two additional vehicles one of which isstopped, and the predicted likely future behavior of the second vehicleis stopping.
 16. The method of claim 15, wherein controlling the firstvehicle includes stopping the first vehicle before making a turningmaneuver.
 17. A system for controlling a first vehicle, the systemcomprising: one or more processors configured to: receive, from anobject sensing system of the vehicle including one or more sensors,information identifying a plurality of mobile objects external to thefirst vehicle as well as position and movement of each of the pluralityof mobile objects; identify an object from the plurality of mobileobjects corresponding to a second vehicle; determine how the firstvehicle would behave if the first vehicle were placed in the position ofthe object; use the determination to predict a likely future behavior ofthe object; and control the first vehicle based on the predicted likelyfuture behavior of the first object.
 18. The system of claim 17, whereinthe one or more processors are further configured to: predict a likelyfuture behavior of a second object of the plurality of moving objects,wherein the second object is a an object other than a vehicle, using anobject-type based prediction model, the movement of the second object,and the position of the second object; and control the vehicle furtherbased on the predicted likely future behavior of the second object. 19.The system of claim 18, wherein the one or more processors are furtherconfigured to, prior to predicting the likely future behavior of thesecond object, determine that a type of the second object is a typeother than a vehicle.
 20. The system of claim 17, further comprising thevehicle.